Identification and Modulation of Microenvironment Is Crucial for Effective Mesenchymal Stromal Cell Therapy in Acute Lung Injury
Bibliographic record
Abstract
Abstract Rationale There are controversial reports on applications of mesenchymal stromal cells (MSCs) in patients with acute respiratory distress syndrome (ARDS). Objectives We hypothesized that lung microenvironment was the main determinant of beneficial versus detrimental effects of MSCs during ARDS. Methods Lung proteome was profiled in three models of injury induced by acid instillation and/or mechanical ventilation in mice. Human gene of glutathione peroxidase-1 was delivered before MSC administration; or MSCs carrying human gene of IL-10 or hepatocyte growth factor were administered after lung injury. An inhibitory cocktail against IL-6, fibronectin, and oxidative stress was used in in vitro studies using human small airway epithelial cells and human MSCs after exposure to plasma of patients with ARDS. Measurements and Main Results Distinct proteomic profiles were observed in three lung injury models. Administration of MSCs protected lung from ventilator-induced injury, whereas it worsened acid-primed lung injuries associated with fibrotic development in lung environment that had high levels of IL-6 and fibronectin along with low antioxidant capacity. Correction of microenvironment with glutathione peroxidase-1, or treatment with MSCs carrying human gene of IL-10 or hepatocyte growth factor after acid-primed injury, reversed the detrimental effects of native MSCs. Proteomic profiles obtained in the mouse models were also similarly observed in human ARDS. Treatment with the inhibitory cocktail in samples of patients with ARDS retained protective effects of MSCs in small airway epithelial cells. Conclusions MSCs can be beneficial or detrimental depending on microenvironment at the time of administration. Identification of potential beneficiaries seems to be crucial to guide MSC therapy in ARDS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".